Identification of a novel, CF-causing compound genotype (p.S1159P and p.Y569H) using an NGS-based assay

Simakova, Tamara; Kondratyeva, E.I.; Avakian, Lusine; Bragin, Anton; Zaytseva, Maria; Павлов, А. Е.

doi:10.1016/j.gene.2015.09.040

Cited by 2 publications

(2 citation statements)

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“…A 38-year-old man with CF diagnosed by sweat testing with a typical clinical course and inconclusive CFTR genotyping became increasingly concerned about his health due to a declining lung function despite strong compliance with self-administered CF therapies. Inspired by the emergence of CFTR mutation-specific pharmacotherapy and the availability of improved genetic testing, this extremely motivated patient sought CFTR gene sequencing analysis, which identified a rare, poorly characterised mutation Ser1159Pro (c.3475T>C) [7] and the common class II mutation Phe508del (c.1521_1523delCTT). He conducted his own personal investigation among the CF patient community and then initiated the further consideration of evaluation and treatment options within his own care team.…”

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confidence: 99%

Personalised CFTR pharmacotherapeutic response testing and therapy of cystic fibrosis

et al. 2018

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confidence: 99%

Personalised CFTR pharmacotherapeutic response testing and therapy of cystic fibrosis

et al. 2018

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“…Results for base calls from the NGS workflow were compared with the results of Sanger sequencing in other studies 2 , 3 , 8 , 18 , 19 . We randomly selected 25 of the 103 patients during the initial discovery stage of variants in silico (Table 3 , Supplementary Table S3 ) to optimize our informatics approach for discovering clinically important variants.…”

Section: Resultsmentioning

confidence: 99%

Prevalence of Rare Genetic Variations and Their Implications in NGS-data Interpretation

Cho

Lee

Jeong³

et al. 2017

Sci Rep

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Next-generation sequencing (NGS) technology has improved enough to discover mutations associated with genetic diseases. Our study evaluated the feasibility of targeted NGS as a primary screening tool to detect causal variants and subsequently predict genetic diseases. We performed parallel computations on 3.7-megabase-targeted regions to detect disease-causing mutations in 103 participants consisting of 81 patients and 22 controls. Data analysis of the participants took about 6 hours using local databases and 200 nodes of a supercomputer. All variants in the selected genes led on average to 3.6 putative diseases for each patient while variants restricted to disease-causing genes identified the correct disease. Notably, only 12% of predicted causal variants were recorded as causal mutations in public databases: 88% had no or insufficient records. In this study, most genetic diseases were caused by rare mutations and public records were inadequate. Most rare variants, however, were not associated with genetic diseases. These data implied that novel, rare variants should not be ignored but interpreted in conjunction with additional clinical data. This step is needed so appropriate advice can be given to primary doctors and parents, thus fulfilling the purpose of this method as a primary screen for rare genetic diseases.

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Identification of a novel, CF-causing compound genotype (p.S1159P and p.Y569H) using an NGS-based assay

Cited by 2 publications

References 4 publications

Personalised CFTR pharmacotherapeutic response testing and therapy of cystic fibrosis

Personalised CFTR pharmacotherapeutic response testing and therapy of cystic fibrosis

Prevalence of Rare Genetic Variations and Their Implications in NGS-data Interpretation

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